Computer systems are constantly improving in terms of speed, reliability, and processing capability. As is known in the art, computer systems which process and store large amounts of data typically include a one or more processors in communication with a shared data storage system in which the data is stored. The data storage system may include one or more storage devices, usually of a fairly robust nature and useful for storage spanning various temporal requirements, e.g. disk drives. The one or more processors perform their respective operations using the storage system. Mass storage systems particular those of the disk array type have centralized data as a hub of operations all driving down costs. But performance demands placed on such mass storage have increased and continue to do so.
Design objective for mass storage systems include cost, performance, and availability. Objectives typically include are a low cost per megabyte, a high I/O performance, and high data availability. Availability is measured by the ability to access data. Often such data availability is provided by use of redundancy such as well-known mirroring techniques.
One problem encountered in the implementation of disk array data storage systems concerns optimizing the storage capacity while maintaining the desired availability and reliability of the data through redundancy. It is important to allocate as closely as possible the right amount of storage capacity with going over or under significantly because of cost and necessity but this is a complex task. It has required great deal of skill and knowledge about computers, software applications such as databases, and the very specialized field of data storage. Such requisite abilities have long been expensive and difficult to access. There remains and probably will be an increasing demand for and corresponding scarcity of such skilled people.
Determining the size and number of disk array or other data storage system needed by a customer requires information about both space, traffic and a desired quality of service. It is not sufficient to size a solution simply based on the perceived quantity of capacity desired, such as the number of terabytes believed to be adequate.
In addition to general capacity planning problems with data storage systems other complexities may arise. One such complexity arise when there is an interest in merging existing data storage systems with newer or fewer data storage systems. Such consolidation helps a customer reduce operating costs by decreasing the number of storage systems, and migrating the data to fewer and/or newer, larger capacity, more efficient storage systems. Consolidation may be of interest for some of the following reasons: saving footprint space; reducing the cost of utilities; simplifying management and reducing management costs; reducing hardware inventory costs; and upgrading to new technology. On the other hand there are costs associated with such a change and there is need for a simple tool to allow for the analysis of such a change and to determine an optimum solution, and in particular with regard to performance requirements and desired characteristics.
There is a long-felt need for a computer-based tool that would allow a straight-forward non-complex way to allocate proper storage capacity while balancing cost, growth plans, workload, and performance requirements and further if such a tool would be useful for analyzing consolidation or merging operations. This would be advancement in the computer arts with particular relevance in the field of data storage.